Build a walk-forward validation plan that separates training, tuning, testing, and rolling deployment windows.
timeline-walkthrough before-after now-you-try A daily equity model needs validation for a live monthly retrain. The team has six years of data and wants to avoid repeatedly tuning on the same future period. Walk-forward validation: train on past data, tune within the research window, test on the next unseen period, then roll forward. The common trap is calling a period out-of-sample after it has been used repeatedly to choose features, thresholds, or filters. Define cadence Set live retrain cadence to monthly, so validation should roll in monthly or quarterly steps. Validation should resemble deployment. A one-time static split may not answer…
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